March 13, 2018

The energy here is palpable.

That’s a phrase so cliché that I cringe a little from using it. But, events like these are what called this phrase into existence. Cliché or not, it’s appropriate here.

One of the funny things about SXSW is something that I hadn’t heard people talk about: It’s kind of a mess. From long, long lines at sessions to parties that you didn’t know you wouldn’t be able to get into to chance encounters with some kind of street team in head-to-toe body paint to “wait, was that Owen Wilson?”…it’s all a little bit random.

Because this is a festival for nerds, I overheard two guys in the line for a panel discussion on UX critiquing the poor user experience of the line we were waiting in. My first reaction was amusement, followed by agreement. Then, slowly, that comment started to feel wrong. SXSW is beautiful and fun not in spite of its randomness, but because of it. We should not design to eliminate the random, sloppy, beautiful stuff that makes us human.

Takeaway #1: AI really isn’t replacing humans any time soon, but will definitely shake things up

There are two equally overblown comments that are made about AI these days. One says that AI will ultimately be able to take every human job, and the other says that there are some jobs that are “immune” to the influence of AI. I was lucky enough to see both of these arguments broken down, in two separate examples from today’s talks.

AI isn’t as good (yet) as we expect

Most people who are not well-versed in AI (myself included) have a generalized fear of what it will mean for their own careers. On the other side of that equation, experienced data scientists, people who are very well-versed in artificial intelligence, seem to underestimate the complexity of human tasks and therefore overestimate the ability of AI to accomplish that same task.

Generally speaking, the AI was able to get the gist and could maybe have been a passable low-cost alternative to human-coded sentiment analysis. However, the AI was also only 25% accurate when compared to an identical transcript that had been coded by a human UX researcher. In short, humans are way, way better at this stuff.

One important reason for this is that the human was able to take other context clues (ie. traffic conditions, audio clues) as inputs into their sentiment analysis. Humans wouldn’t often mistake frustration for joy, but the AI did.

The important caveat is this: given time and more sophisticated iterations of facial sentiment analysis AIs, it doesn’t seem like this specific piece of UX work will remain a human function forever.

Further, the AI used in this experiment was over 5x less expensive, and much faster overall, than the human UX researcher who worked on the project. As the technology improves, the incentive to replace human researchers with AI will grow ever stronger.

AI can be more creative than we think

On the other side of the spectrum, people often say “artificial intelligence will never take the place of human creativity.” There are plenty of ways in which this claim seems true. At least up until this point, AI has shown itself to be spectacularly poor at recreating human creative processes.

AI excels at processing very large amounts of data. This can be seen as a “raw input” into the creative process; the basic building blocks of creative free association. From those raw building blocks, AI can assist in generating novel creative combinations between seemingly disparate topics.

Yet humans do have an advantage in creativity because the brain associates those building blocks not just rationally, but emotionally. A particularly anxiety-inducing memory (let’s say it’s a rough public speaking event) is linked in the brain not just to other failed public speaking events, but to a complex web of other anxiety-inducing events.

So the human brain has a unique lease on emotional associations, and will ultimately have something like a final say-so in judging the effectiveness of those AI-generated creative combinations. However, as a creative AI is trained, it will likely become better and better at developing “successful” creative combinations. As this success improves, the partnership between AI and creative professionals will continually lighten the cognitive load of creative work.

Interestingly, in many sessions today, AI professionals discussed some frustration about the unavailability of more comprehensive data ecosystems on which to train their models. For example, imagine, in either of the above scenarios, an AI problem space that included neuro-chemical feedback from their test subjects. It seems clear that IoT and more sophisticated data collection will ultimately remove some of these significant AI barriers and will stimulate growth well beyond the use cases where AI has been leveraged to-date.

Takeaway #2: Data professionals still struggle with “translation”

At a Data Nerd meet-up, with attendees spanning many industries, many job functions, and many experience levels, there was a surprising consensus about one key thing: most people don’t understand data. Some common frustrations were echoed across the room: “people don’t use the dashboards I created”, “senior executives don’t act on the insights that we generate for them”, “my organization isn’t investing enough resources in our data team”, etc.

What all of these complaints ultimately come down to is that simple data literacy is lacking, even in markets where it’s critical to achieving success.

From my perspective, the implication here is both simple and profound. Data leaders who are able to produce culture change and also implement smart data solutions will win. It doesn’t matter whether those leaders are in-house evangelists, or agency/consultant partners.

Strong data leadership will produce early-adoption benefits for some organizations, while weak leadership will pin others down. I don’t think it’s too grand to say that this distinction is now, and will continue to be, a core market differentiator.

Key Takeaway #3: There is deep power in standing for something and cultivating communities

Strong feelings of community are some of the most motivating emotions that we feel as humans. Leveraging community and social movements can be an interesting opportunity for brands, but the way this gets executed can be so tone deaf and cringe-worthy.

So while many marketers frame this struggle as “an authenticity problem”, the reality is this: You need to actually care.

In my mind, Patagonia is a clear standout for their commitment to environmentalism. The reason for that is simple: Chouinard was an outdoorsman first and above everything else. He transformed the tools that rock climbers use in order to minimize the damage to the environment and he literally wrote the book on social enterprise business. Patagonia is deeply hands-on with minimizing environmental harm in their supply chain, their Worn Wear program actively works to repair or recycle used gear rather than letting it end up in the waste stream, and they’ve become a loud voice for protecting public land.

Rather than fixing an “authenticity problem”, Patagonia engrains sustainability as a core value of the company. That commitment shines through in everything else that they do.

Similarly, in a panel discussion of food-related female entrepreneurs (How to Build a Digital Brand and Cultivate Community), the best piece of advice we heard was this: once a business has formed or found an online community for their brand, they need to stand by that community at all costs.

That seems obvious, but the panelists spoke at length about just how difficult this can be. Lucrative sponsorships and co-promotion deals are hard to turn down, but if that promotion serves the business rather than the community then it can be toxic to your loyalty and to your bottom line.

As a side note, the “how” on creating strong online/offline communities is fascinating stuff and requires lots of fun data-crunching.

So the big, overarching takeaway from the day is that no matter how smart your data strategy is, no matter how sophisticated your algorithm, you need to keep real human beings at the center of everything that you do.

Which brings me back to the shape of that line (the one with the bad UX). I’d argue that its coiling, free-ranging form was actually responsible for bringing those two UXers together and facilitating their connection.

If you want to design for pure, machine-like efficiency, by all means fix the line. But if you’re designing for humanity, and for the chance interaction between strangers, then just leave the mess alone.